Over the past decade the advent of smaller, cheaper range sensors has made it more attractive to field robots that can acquire 3D range maps of their environment. Early systems made use of single scan range finders such as the SICK or Hokuyo sensors which were mounted on moving platforms or pan-tilt heads and scanned across the scene to produce a 3D point cloud. More recently, range sensors such at the SR 4000 ‘Swiss Ranger’ from Mesa Imaging and the Velodyne scanning range sensor have been used to produce two dimensional range images at high frame rates.
The recently announced 2D range camera systems from Canesta and Primesense promise to further accelerate this trend by providing real time range imagery at a very compelling price point. The Primesense sensor, which will be employed in the Xbox Kinect system, is a particularly interesting example since it acquires a color video stream along with the range imagery which makes it easier to deploy schemes that exploit both sources of information simultaneously.
It is desirable to endow robots with the ability to extract relevant high level percepts from the stream of sensor data. For instance, in an indoor environment it would be useful for the robot to be able to quickly detect relevant objects such as walls, doors, windows, tables and chairs.
One issue with prior art segmentation methods is that they require a considerable amount of computational effort and, as such, they are not ideally suited to a robotic context where we are typically interested in processing large volumes of data quickly. It is desirable to produce competitive segmentation results in real time with modest computational effort.